Modeling and Optimization of Latency in Erasure-coded Storage Systems

Abstract

As consumers are increasingly engaged in social networking and E-commerce activities, businesses grow to rely on Big Data analytics for intelligence, and traditional IT infrastructures continue to migrate to the cloud and edge, these trends cause distributed data storage demand to rise at an unprecedented speed. Erasure coding has seen itself quickly emerged as a promising technique to reduce storage cost while providing similar reliability as replicated systems, widely adopted by companies like Facebook, Microsoft and Google. However, it also brings new challenges in characterizing and optimizing the access latency when erasure codes are used in distributed storage. The aim of this monograph is to provide a review of recent progress (both theoretical and practical) on systems that employ erasure codes for distributed storage.In this monograph, we will first identify the key challenges and taxonomy of the research problems and then give an overview of different approaches that have been developed to quantify and model latency of erasure-coded storage. This includes recent work leveraging MDS-Reservation, Fork-Join, Probabilistic, and Delayed-Relaunch scheduling policies, as well as their applications to characterize access latency (e.g., mean, tail, asymptotic latency) of erasure-coded distributed storage systems. We will also extend the problem to the case when users are streaming videos from erasure-coded distributed storage systems. Next, we bridge the gap between theory and practice, and discuss lessons learned from prototype implementation. In particular, we will discuss exemplary implementations of erasure-coded storage, illuminate key design degrees of freedom and tradeoffs, and summarize remaining challenges in real-world storage systems such as in content delivery and caching. Open problems for future research are discussed at the end of each chapter.

Explorando Técnicas de Compressão para Melhorar a Eficiência de Tratamento de Dados IoT

“…Nesse contexto, o armazenamento de dados nuvem constitui um grande avanc ¸o, devido aos seus vastos recursos de armazenamento e escalabilidade, oferecendo seguranc ¸a de dados, facilidade de acesso e recuperac ¸ão em caso de desastres. Contudo existe um custo financeiro associado que precisa ser considerado, demandando estratégias adequadas de armazenamento e recuperac ¸ão da informac ¸ão (Aggarwal and Lan 2020).…”